Glassbeam’s business revolves around providing business intelligence on machine data. Intelligence comes from structured data. Machine data is not always structured. So, there is a gap between what is needed and what is produced. As Glassbeam’s head of engineering, I am going to write a two series blog about how Glassbeam bridges this gap.
Capital expenditures for healthcare equipment totaled more than $350 billion in 2016, according to Harbor Research. Healthcare organizations and Independent Service Organizations (ISOs) are now turning to AI and machine learning to predict and prevent equipment failures and reduce operational costs.
Predictive analytics can be used to reclaim millions of dollars in operational costs for healthcare organizations.
As pressure mounts to lower healthcare costs, healthcare delivery organizations are taking a closer look at costs in all aspects of their business, particularly operations. More organizations are realizing there is a huge opportunity to lower operational costs by leveraging machine data and machine learning.
Glassbeam loves its customers. Customer development is an integral part of Glassbeam. Our healthcare/clinical customers are telling us how the newly launched Glassbeam CLinical Engineering ANalytics (CLEAN) Blueprint is creating immense value by analyzing machine log data and presenting deep insights about machines in their clinical environment.
We are exhibiting at MD Expo April 4-6, a leading high tech medicine (HTM) trade show and conference. It will be held at the Renaissance Nashville Hotel, 611 Commerce Street. We hope you will stop and visit us at booth #119.
In my role as the Healthcare Solutions Specialist for Glassbeam I've had the opportunity to meet with a wide range of Glassbeam customers and partners. I've spoken to members of the C-Suite, Directors of Clinical Engineering, and Field Service Engineers/Imaging Service Engineers (FSEs/ISEs) on the frontlines of imaging system service.
In my previous blog, I discussed the framework we have built for testing large scale machine log data. In this post, I will share results of our test. Every test run with varying server clusters was executed through our automation framework, and therefore had minimal effort on our side except clicking a button after deciding the number of servers we need.
The scalability of our platform is broadly dependent on 2 things:
While we run must-run performance tests for every release to make sure that the new features being added does not impact the performance of our platform, we (Engineering @ Glassbeam) wanted the ability to run large scale tests periodically too. However, running large scale performance tests is a time consuming and expensive affair. Such tests require spinning up tens of machines which can take days to setup and run the tests.